Global versus structured interpretation of motion: moving light displays
Bibliographic record
Abstract
Moving light displays (MLDs) have been used extensively to study motion perception and perception of the human gait in particular. MLD perception is largely considered to be structural, i.e., perception depends on identification of human kinematic structure. However, work by Little and Boyd (1996) has shown that it is possible to recognize individual people, from their gaits, by non-structural means. They use global shape-of-motion features derived from optical flow in a sequence of gray-scale images. Our goal is to show that shape-of-motion features can be derived equally well from MLD images as from gray-scale images, and to compare the recent results obtained for shape-of-motion recognition with psychophysical observations about MLD perception. The implication is that non-structural shape-of-motion interpretation of gait can be applied to MLDs, allowing us to interpret significant MLD results in the context of a known algorithm. Our results shed light on the validity of shape-of-motion features from the psychophysical standpoint as well as suggest an alternative approach to understanding MLD perception. In particular we find that characterizing movement in a gait may be treated as the sum of a set of moving points (if this is true then MLD lights need not be placed right at joints). Changes to a subset of the points affect the sum and consequently affect the perception of the whole.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".